---
title: "Anxiety Trend Dashboard"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
source: embed
theme: journal
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(viridis)
library(plotly)
options(
ggplot2.continuous.colour = "viridis",
ggplot2.continuous.fill = "viridis"
)
scale_colour_discrete = scale_colour_viridis_d
scale_fill_discrete = scale_fill_viridis_d
theme_set(
theme_minimal() +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.5)
)
)
```
```{r}
depression =
read_csv("data/nhis_data01.csv") %>%
janitor::clean_names() %>%
filter(year>=2015) %>%
select(year, deprx, depfreq, depfeelevl, age, sex, marst, poverty) %>%
mutate(
sex = recode_factor(sex,
"1" = "Male",
"2" = "Female"),
marst = recode_factor(marst,
"10" = "Married", "11" = "Married", "12" = "Married", "13" = "Married",
"20" = "Widowed",
"30" = "Divorced",
"40" = "Separated",
"50" = "Never married"),
poverty = recode_factor(poverty,
"11" = "Less than 1.0", "12" = "Less than 1.0",
"13" = "Less than 1.0", "14" = "Less than 1.0",
"21" = "1.0-2.0", "22" = "1.0-2.0",
"23" = "1.0-2.0", "24" = "1.0-2.0",
"25" = "1.0-2.0",
"31" = "2.0 and above","32" = "2.0 and above",
"33" = "2.0 and above","34" = "2.0 and above",
"35" = "2.0 and above","36" = "2.0 and above",
"37" = "2.0 and above","38" = "2.0 and above"),
deprx = recode_factor(deprx, '1' = "no", '2' = "yes"),
depfreq = recode_factor(depfreq, '1' = "Daily", '2' = "Weekly",
'3' = "Monthly", '4' = "A few times a year",
'5' = "Never"),
depfeelevl = recode_factor(depfeelevl, '1' = "A lot",
'3' = "Somewhere between a little and a lot",
'2' = "A little")
) %>%
filter_at(vars(deprx, depfreq, depfeelevl), any_vars(!is.na(.)))
```
Percentage of Depression
=====================================
Column {data-width=650}
-----------------------------------------------------------------------
### Percentage of people reported taken medication for depression
```{r}
depression %>%
drop_na(deprx) %>%
group_by(year, deprx) %>%
summarize(dep_num = n()) %>%
pivot_wider(
names_from = deprx,
values_from = dep_num
) %>%
mutate(
dep_percentage = yes/(no + yes)*100,
text_label = str_c(yes, " out of ", no + yes)
) %>%
ungroup() %>%
plot_ly(
y = ~dep_percentage,
x = ~year,
color = ~year,
type = "bar",
colors = "viridis",
text = ~text_label
) %>%
layout(
xaxis = list (title = ""),
yaxis = list (title = "Percentage"),
showlegend = FALSE
) %>%
hide_colorbar()
```
Column {.tabset}
-------------------------------------
### By Biological sex
```{r}
depression %>%
drop_na(sex, deprx) %>%
group_by(sex, year, deprx) %>%
summarize(dep_num = n()) %>%
pivot_wider(
names_from = deprx,
values_from = dep_num
) %>%
mutate(
dep_percentage = yes/(no + yes)*100,
text_label = str_c(yes, " out of ", no + yes)
) %>%
ungroup() %>%
plot_ly(
y = ~dep_percentage,
x = ~year,
color = ~sex,
type = "bar",
colors = "viridis",
text = ~text_label
) %>%
add_trace(
x = ~year,
y = ~dep_percentage,
color = ~sex,
type='scatter',
mode='lines+markers'
) %>%
layout(
xaxis = list (title = ""),
yaxis = list (title = "Percentage"),
legend = list(orientation = 'h')
)
```
### By household income to poverty line ratio
```{r}
depression %>%
drop_na(poverty, deprx) %>%
group_by(poverty, year, deprx) %>%
summarize(dep_num = n()) %>%
pivot_wider(
names_from = deprx,
values_from = dep_num
) %>%
mutate(
dep_percentage = yes/(no + yes)*100,
text_label = str_c(yes, " out of ", no + yes)
) %>%
ungroup() %>%
plot_ly(
y = ~dep_percentage,
x = ~year,
color = ~poverty,
type = "scatter",
mode = "lines+markers",
colors = "viridis",
text = ~text_label
) %>%
layout(
xaxis = list (title = ""),
yaxis = list (title = "Percentage"),
legend = list(orientation = 'h')
)
```
### By martial status
```{r}
depression %>%
drop_na(marst, deprx) %>%
group_by(marst, year, deprx) %>%
summarize(dep_num = n()) %>%
pivot_wider(
names_from = deprx,
values_from = dep_num
) %>%
mutate(
dep_percentage = yes/(no + yes)*100,
text_label = str_c(yes, " out of ", no + yes)
) %>%
ungroup() %>%
plot_ly(
y = ~dep_percentage,
x = ~year,
color = ~marst,
type = "scatter",
mode='lines+markers',
colors = "viridis",
text = ~text_label
) %>%
layout(
xaxis = list (title = ""),
yaxis = list (title = "Percentage"),
legend = list(orientation = 'h')
)
```
Level and Frequency of Depression {data-orientation=columns}
=====================================
Column {data-width=500}
-----------------------------------------------------------------------
### Level of depression
```{r,echo = FALSE, message=FALSE}
depression %>%
drop_na(depfeelevl) %>%
group_by(year, depfeelevl) %>%
summarize(count = n()) %>%
group_by(year) %>%
summarize(
percentage=100 * count/sum(count),
sum_count = sum(count),
depfeelevl = depfeelevl,
count=count
) %>%
mutate(
text_label = str_c(count, " out of ", sum_count)
) %>%
plot_ly(
y = ~percentage,
x = ~year,
color = ~depfeelevl,
type = "bar",
colors = "viridis",
text = ~text_label
) %>%
layout(
xaxis = list (title = ""),
yaxis = list (title = "Percentage"),
barmode = 'stack',
legend = list(orientation = 'h')
)
```
Column {data-width=500}
-----------------------------------------------------------------------
### Frequency of depression
```{r,echo = FALSE, message=FALSE}
depression %>%
drop_na(depfreq) %>%
group_by(year, depfreq) %>%
summarize(count = n()) %>%
group_by(year) %>%
summarize(
percentage=100 * count/sum(count),
sum_count = sum(count),
depfreq = depfreq,
count=count
) %>%
mutate(
text_label = str_c(count, " out of ", sum_count)
) %>%
plot_ly(
y = ~percentage,
x = ~year,
color = ~depfreq,
type = "bar",
colors = "viridis",
text = ~text_label
) %>%
layout(
xaxis = list (title = ""),
yaxis = list (title = "Percentage"),
barmode = 'stack',
legend = list(orientation = 'h')
)
```